Hi everyone, I hope you can clarify this to me.
I have a neural network that needs to predict five classes. One of those classes is very easy to identify since one of the features is highly connected to that class. So when I train the network, it can predict that mentioned class, but it can not differentiate the others.
Lets say my classes are 0,1,2,3 and 4 and in the predictions i have these values:
[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 4, 0, 4, 4, 4, 0, 4, 4, 0, 4, 4,
4, 4, 0, 4, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 4, 0, 0, 0, 0, 0, 4, 4, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4,
0, 4, 4, 0, 4, 4, 0, 4, 0, 4, 4, 4, 0, 0, 4, 4, 0, 0, 4, 0, 0, 0, 0, 0]
while the real outputs are like this:
[4, 1, 3, 1, 1, 4, 4, 2, 1, 2, 1, 0, 0, 1, 0, 4, 1, 1, 0, 4, 4, 0, 3, 1,
4, 4, 0, 4, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 3, 4, 2, 4, 3, 4, 2, 4,
1, 1, 3, 2, 4, 3, 3, 2, 4, 0, 1, 4, 0, 2, 2, 0, 0, 0, 0, 0, 4, 4, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 4, 4, 4, 1, 4, 0, 4, 0, 4, 4, 3, 3, 2, 1,
0, 3, 2, 0, 3, 3, 0, 2, 0, 4, 3, 4, 0, 0, 4, 3, 0, 0, 1, 0, 0, 0, 0, 0]
As you can see class "0" is always predicted correctly.
Should I then maybe take out this class from the training and just train the nn it with the rest of them?
Is it possible that since class "0" has a straightforward pattern to detect is not allowing the nn to learn the behaviour of the other classes?
I would appreciate it if you could give me some light on this.
submitted by /u/CardiologistGlass934
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